Development of an Intelligent Controlled Water Irrigation System With Leak Detection

Irrigation is a critical component of agricultural productivity, yet conventional systems often face inefficiencies such as excessive water usage, undetected leaks, and a lack of real-time control. These challenges are particularly pressing for small and medium-scale farms, where limited resources necessitate cost-effective solutions. To address these issues, this project aims to develop an intelligent water management system capable of monitoring, controlling, and optimising water distribution while detecting and mitigating leaks. The primary objectives include collecting data to train and test a machine learning model, designing an intelligent irrigation system, implementing the proposed solution, and evaluating its performance. By integrating advanced monitoring and automated control, the system seeks to enhance water efficiency, reduce waste, and support sustainable farming practices in water-sensitive environments.
The proposed system combines hardware and software components to achieve intelligent water management with leak detection. Hardware includes YF-S401 water flow sensors, solenoid valves, and an Arduino Mega microcontroller connected to an ESP-01 Wi-Fi module. Software is built using Django (backend), Python with supervised machine learning algorithms (SVM, Random Forest, Decision Tree, XGBoost, CATBoost), SQLite database, and a responsive web interface for user interaction. Real-time sensor data is sent via HTTP requests to a cloud-hosted model on PythonAnywhere, where it is analysed to classify leak scenarios. When a leak is detected, automated valve control is triggered to prevent further water loss, and system status is updated on the web platform.
The system was rigorously tested to evaluate its performance in real-time leak detection and response. A total of 20 test trials were conducted under various simulated conditions, including "no leak", "leak in Zone A", "leak in Zone B", and "leak in both zones". The CATBoost model emerged as the best-performing classifier after hyperparameter tuning, achieving an accuracy of 90.8%, precision of 92%, and an AUC score of 0.977, with a response time of 0.0064 seconds. This model was deployed on a cloud-based server for real-time inference. During testing, the system successfully classified leaks within an average of 1.12 seconds and triggered the appropriate solenoid valve closure within 2-3 seconds of detection, effectively minimising water loss. Data transmission between the Arduino Mega microcontroller and the Django backend via the ESP-01 Wi-Fi module was stable, with no significant latency observed. The web interface, built using React JS and Bootstrap, provided real-time visual feedback and allowed manual override, ensuring user control and system transparency. These results demonstrate that the system can reliably detect leaks and respond promptly, making it suitable for practical deployment in small to medium-scale agricultural settings.
This solution has potential applications in precision agriculture, greenhouse farming, and other water-sensitive environments. By improving water resource management and reducing operational inefficiencies, it supports sustainable farming practices and contributes to climate-resilient agricultural production. Its cost-effective design makes it accessible for adoption in small to medium-scale farms, particularly in regions facing water scarcity.